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Topics in Nonparametric Statistics.

Chang, Christopher.

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Topics in Nonparametric Statistics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Topics in Nonparametric Statistics./
Author:	Chang, Christopher.
Description:	83 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .
Contained By:	Dissertation Abstracts International72-08B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3456923
ISBN:	9781124667560

Topics in Nonparametric Statistics.
Chang, Christopher.

Topics in Nonparametric Statistics. - 83 p.

Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .

Thesis (Ph.D.)--University of California, San Diego, 2011.

This thesis is concerned with nonparametric techniques for inferring properties of time series.

ISBN: 9781124667560Subjects--Topical Terms:

517247
Statistics.

Topics in Nonparametric Statistics.
LDR:02877nam 2200349 4500 001 1402528
005 20111102140023.5
008 130515s2011 ||||||||||||||||| ||eng d
020 $a 9781124667560
035 $a (UMI)AAI3456923
035 $a AAI3456923
040 $a UMI $c UMI
100 1 $a Chang, Christopher. $3 1681723
245 1 0 $a Topics in Nonparametric Statistics.
300 $a 83 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .
500 $a Adviser: Dimitris N. Politis.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2011.
520 $a This thesis is concerned with nonparametric techniques for inferring properties of time series.
520 $a First, we consider finite-order moving average and nonlinear autoregressive processes with no parametric assumption on the innovation distribution, and present a kernel density estimator of a bootstrap series that estimates their marginal densities root-n consistently. This is equal to the rate of the best known convolution estimators, and faster than the standard kernel density estimator. We also conduct simulations to check the finite sample properties of our estimator, and the results are generally better than corresponding results for the standard kernel density estimator.
520 $a Next, given stationary time series data, we study the problem of finding the best linear combination of a set of lag window spectral density estimators with respect to the mean squared risk. We present an aggregation procedure and prove a sharp oracle inequality for its risk. We also provide simulations demonstrating the performance of our aggregation procedure, given Bartlett and other estimators of varying bandwidths as input. This extends work by Rigollet and Tsybakov on aggregation of density estimators.
520 $a The last part of this thesis introduces a class of robust autocorrelation estimators based on interpreting the sample autocorrelation function as a linear regression. We investigate the efficiency and robustness properties of the estimators that result from plugging on three common robust regression techniques. Construction of robust autocovariance and positive definite autocorrelation estimates is discussed, as well as application of the estimators to AR model fitting. We finish with simulations, which suggest that the estimators are especially well suited for AR model fitting.
590 $a School code: 0033.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a University of California, San Diego. $b Mathematics. $3 1022364
773 0 $t Dissertation Abstracts International $g 72-08B.
790 1 0 $a Politis, Dimitris N., $e advisor
790 1 0 $a Abramson, Ian S. $e committee member
790 1 0 $a Arias-Castro, Ery $e committee member
790 1 0 $a Gamst, Anthony $e committee member
790 1 0 $a Messer, Karen $e committee member
790 $a 0033
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3456923